{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 项目：用线性回归预测房价数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分析目标"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "此数据分析报告的目的是，基于已有的房屋销售价格，以及有关该房屋的属性，进行线性回归分析，从而利用得到的线性回归模型，能对以下未知售价的房屋根据属性进行价格预测：\n",
    "\n",
    "面积为6500平方英尺，有4个卧室、2个厕所，总共2层，不位于主路，无客人房，带地下室，有热水器，没有空调，车位数为2，位于城市首选社区，简装修。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 简介"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据集`house_price.csv`记录了超过五百栋房屋的交易价格，以及房屋的相关属性信息，包括房屋面积、卧室数、厕所数、楼层数、是否位于主路、是否有客房，等等。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`house_price.csv`每列的含义如下：\n",
    "- price：房屋出售价格\n",
    "- area：房屋面积，以平方英尺为单位\n",
    "- bedrooms：卧室数\n",
    "- bathrooms：厕所数\n",
    "- stories：楼层数\n",
    "- mainroad：是否位于主路\n",
    "   - yes  是\n",
    "   - no\t  否\n",
    "- guestroom：是否有客房\n",
    "   - yes  是\n",
    "   - no\t  否\n",
    "- basement：是否有地下室\n",
    "   - yes  是\n",
    "   - no\t  否\n",
    "- hotwaterheating：是否有热水器\n",
    "   - yes  是\n",
    "   - no\t  否\n",
    "- airconditioning：是否有空调\n",
    "   - yes  是\n",
    "   - no\t  否\n",
    "- parking：车库容量，以车辆数量为单位\n",
    "- prefarea：是否位于城市首选社区\n",
    "   - yes  是\n",
    "   - no\t  否\n",
    "- furnishingstatus：装修状态\n",
    "   - furnished       精装\n",
    "   - semi-furnished\t 简装\n",
    "   - unfurnished     毛坯"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "导入数据分析所需要的库，并通过Pandas的`read_csv`函数，将原始数据文件\"house_price.csv\"里的数据内容，解析为DataFrame并赋值给变量`original_house_price`。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
       "      <th>basement</th>\n",
       "      <th>hotwaterheating</th>\n",
       "      <th>airconditioning</th>\n",
       "      <th>parking</th>\n",
       "      <th>prefarea</th>\n",
       "      <th>furnishingstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13300000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12250000</td>\n",
       "      <td>8960</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12250000</td>\n",
       "      <td>9960</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12215000</td>\n",
       "      <td>7500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11410000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
       "0  13300000  7420         4          2        3      yes        no       no   \n",
       "1  12250000  8960         4          4        4      yes        no       no   \n",
       "2  12250000  9960         3          2        2      yes        no      yes   \n",
       "3  12215000  7500         4          2        2      yes        no      yes   \n",
       "4  11410000  7420         4          1        2      yes       yes      yes   \n",
       "\n",
       "  hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
       "0              no             yes        2      yes        furnished  \n",
       "1              no             yes        3       no        furnished  \n",
       "2              no              no        2      yes   semi-furnished  \n",
       "3              no             yes        3      yes        furnished  \n",
       "4              no             yes        2       no        furnished  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "original_house_price = pd.read_csv(\"house_price.csv\")\n",
    "original_house_price.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 评估和清理数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这一部分中，我们将对在上一部分建立的`original_house_price`DataFrame所包含的数据进行评估和清理。\n",
    "\n",
    "主要从两个方面进行：结构和内容，即整齐度和干净度。\n",
    "\n",
    "数据的结构性问题指不符合“每个变量为一列，每个观察值为一行，每种类型的观察单位为一个表格”这三个标准；数据的内容性问题包括存在丢失数据、重复数据、无效数据等。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了区分开经过清理的数据和原始的数据，我们创建新的变量`cleaned_house_price`，让它为`original_house_price`复制出的副本。我们之后的清理步骤都将被运用在`cleaned_house_price`上。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_house_price = original_house_price.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据整齐度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
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       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
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       "      <td>yes</td>\n",
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       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
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       "      <th>2</th>\n",
       "      <td>12250000</td>\n",
       "      <td>9960</td>\n",
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       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12215000</td>\n",
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       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
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       "      <th>4</th>\n",
       "      <td>11410000</td>\n",
       "      <td>7420</td>\n",
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       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10850000</td>\n",
       "      <td>7500</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10150000</td>\n",
       "      <td>8580</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10150000</td>\n",
       "      <td>16200</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>unfurnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9870000</td>\n",
       "      <td>8100</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9800000</td>\n",
       "      <td>5750</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>unfurnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9800000</td>\n",
       "      <td>13200</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9681000</td>\n",
       "      <td>6000</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9310000</td>\n",
       "      <td>6550</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>9240000</td>\n",
       "      <td>3500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>9240000</td>\n",
       "      <td>7800</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       price   area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
       "0   13300000   7420         4          2        3      yes        no       no   \n",
       "1   12250000   8960         4          4        4      yes        no       no   \n",
       "2   12250000   9960         3          2        2      yes        no      yes   \n",
       "3   12215000   7500         4          2        2      yes        no      yes   \n",
       "4   11410000   7420         4          1        2      yes       yes      yes   \n",
       "5   10850000   7500         3          3        1      yes        no      yes   \n",
       "6   10150000   8580         4          3        4      yes        no       no   \n",
       "7   10150000  16200         5          3        2      yes        no       no   \n",
       "8    9870000   8100         4          1        2      yes       yes      yes   \n",
       "9    9800000   5750         3          2        4      yes       yes       no   \n",
       "10   9800000  13200         3          1        2      yes        no      yes   \n",
       "11   9681000   6000         4          3        2      yes       yes      yes   \n",
       "12   9310000   6550         4          2        2      yes        no       no   \n",
       "13   9240000   3500         4          2        2      yes        no       no   \n",
       "14   9240000   7800         3          2        2      yes        no       no   \n",
       "\n",
       "   hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
       "0               no             yes        2      yes        furnished  \n",
       "1               no             yes        3       no        furnished  \n",
       "2               no              no        2      yes   semi-furnished  \n",
       "3               no             yes        3      yes        furnished  \n",
       "4               no             yes        2       no        furnished  \n",
       "5               no             yes        2      yes   semi-furnished  \n",
       "6               no             yes        2      yes   semi-furnished  \n",
       "7               no              no        0       no      unfurnished  \n",
       "8               no             yes        2      yes        furnished  \n",
       "9               no             yes        1      yes      unfurnished  \n",
       "10              no             yes        2      yes        furnished  \n",
       "11             yes              no        2       no   semi-furnished  \n",
       "12              no             yes        1      yes   semi-furnished  \n",
       "13             yes              no        2       no        furnished  \n",
       "14              no              no        0      yes   semi-furnished  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_house_price.head(15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据干净度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来通过`info`，对数据内容进行大致了解。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 545 entries, 0 to 544\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   price             545 non-null    int64 \n",
      " 1   area              545 non-null    int64 \n",
      " 2   bedrooms          545 non-null    int64 \n",
      " 3   bathrooms         545 non-null    int64 \n",
      " 4   stories           545 non-null    int64 \n",
      " 5   mainroad          545 non-null    object\n",
      " 6   guestroom         545 non-null    object\n",
      " 7   basement          545 non-null    object\n",
      " 8   hotwaterheating   545 non-null    object\n",
      " 9   airconditioning   545 non-null    object\n",
      " 10  parking           545 non-null    int64 \n",
      " 11  prefarea          545 non-null    object\n",
      " 12  furnishingstatus  545 non-null    object\n",
      "dtypes: int64(6), object(7)\n",
      "memory usage: 55.5+ KB\n"
     ]
    }
   ],
   "source": [
    "cleaned_house_price.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从输出结果来看，cleaned_house_price共有545条观察值，变量不存在缺失值。\n",
    "\n",
    "数据类型方面，我们已知mainroad（是否位于主路）、guestroom（是否有客房）、basement（是否有地下室）、hotwaterheating（是否有热水器）、airconditioning（是否有空调）、prefarea（是否位于城市首选社区）、furnishingstatus（装修状态）都是分类数据，可以把数据类型都转换为Category。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_house_price['mainroad'] = cleaned_house_price['mainroad'].astype(\"category\")\n",
    "cleaned_house_price['guestroom'] = cleaned_house_price['guestroom'].astype(\"category\")\n",
    "cleaned_house_price['basement'] = cleaned_house_price['basement'].astype(\"category\")\n",
    "cleaned_house_price['hotwaterheating'] = cleaned_house_price['hotwaterheating'].astype(\"category\")\n",
    "cleaned_house_price['airconditioning'] = cleaned_house_price['airconditioning'].astype(\"category\")\n",
    "cleaned_house_price['prefarea'] = cleaned_house_price['prefarea'].astype(\"category\")\n",
    "cleaned_house_price['furnishingstatus'] = cleaned_house_price['furnishingstatus'].astype(\"category\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 545 entries, 0 to 544\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype   \n",
      "---  ------            --------------  -----   \n",
      " 0   price             545 non-null    int64   \n",
      " 1   area              545 non-null    int64   \n",
      " 2   bedrooms          545 non-null    int64   \n",
      " 3   bathrooms         545 non-null    int64   \n",
      " 4   stories           545 non-null    int64   \n",
      " 5   mainroad          545 non-null    category\n",
      " 6   guestroom         545 non-null    category\n",
      " 7   basement          545 non-null    category\n",
      " 8   hotwaterheating   545 non-null    category\n",
      " 9   airconditioning   545 non-null    category\n",
      " 10  parking           545 non-null    int64   \n",
      " 11  prefarea          545 non-null    category\n",
      " 12  furnishingstatus  545 non-null    category\n",
      "dtypes: category(7), int64(6)\n",
      "memory usage: 30.3 KB\n"
     ]
    }
   ],
   "source": [
    "cleaned_house_price.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 处理缺失数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从`info`方法的输出结果来看，`cleaned_house_price`不存在缺失值，因此不需要对缺失数据进行处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 处理重复数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据数据变量的含义以及内容来看，允许变量重复，我们不需要对此数据检查是否存在重复值。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 处理不一致数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "不一致数据可能存在于所有分类变量中，我们要查看是否存在不同值实际指代同一目标的情况。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mainroad\n",
       "yes    468\n",
       "no      77\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_house_price[\"mainroad\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "guestroom\n",
       "no     448\n",
       "yes     97\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_house_price[\"guestroom\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "basement\n",
       "no     354\n",
       "yes    191\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_house_price[\"basement\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hotwaterheating\n",
       "no     520\n",
       "yes     25\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_house_price[\"hotwaterheating\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "airconditioning\n",
       "no     373\n",
       "yes    172\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_house_price[\"airconditioning\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "prefarea\n",
       "no     417\n",
       "yes    128\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_house_price[\"prefarea\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "furnishingstatus\n",
       "semi-furnished    227\n",
       "unfurnished       178\n",
       "furnished         140\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_house_price[\"furnishingstatus\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从以上输出结果来看，均不存在不一致数据。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 处理无效或错误数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以通过DataFrame的`describe`方法，对数值统计信息进行快速了解。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>parking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.450000e+02</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.766729e+06</td>\n",
       "      <td>5150.541284</td>\n",
       "      <td>2.965138</td>\n",
       "      <td>1.286239</td>\n",
       "      <td>1.805505</td>\n",
       "      <td>0.693578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.870440e+06</td>\n",
       "      <td>2170.141023</td>\n",
       "      <td>0.738064</td>\n",
       "      <td>0.502470</td>\n",
       "      <td>0.867492</td>\n",
       "      <td>0.861586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.750000e+06</td>\n",
       "      <td>1650.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.430000e+06</td>\n",
       "      <td>3600.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.340000e+06</td>\n",
       "      <td>4600.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.740000e+06</td>\n",
       "      <td>6360.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.330000e+07</td>\n",
       "      <td>16200.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price          area    bedrooms   bathrooms     stories  \\\n",
       "count  5.450000e+02    545.000000  545.000000  545.000000  545.000000   \n",
       "mean   4.766729e+06   5150.541284    2.965138    1.286239    1.805505   \n",
       "std    1.870440e+06   2170.141023    0.738064    0.502470    0.867492   \n",
       "min    1.750000e+06   1650.000000    1.000000    1.000000    1.000000   \n",
       "25%    3.430000e+06   3600.000000    2.000000    1.000000    1.000000   \n",
       "50%    4.340000e+06   4600.000000    3.000000    1.000000    2.000000   \n",
       "75%    5.740000e+06   6360.000000    3.000000    2.000000    2.000000   \n",
       "max    1.330000e+07  16200.000000    6.000000    4.000000    4.000000   \n",
       "\n",
       "          parking  \n",
       "count  545.000000  \n",
       "mean     0.693578  \n",
       "std      0.861586  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      0.000000  \n",
       "75%      1.000000  \n",
       "max      3.000000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_house_price.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从以上统计信息来看，`cleaned_house_price`里不存在脱离现实意义的数值。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 探索数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在着手推断统计学分析之前，我们可以先借助数据可视化，探索数值变量的分布，以及与房价存在相关性的变量，为后续的进一步分析提供方向。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_palette(\"pastel\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 房价分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [7.00, 3.50]\n",
    "plt.rcParams[\"figure.autolayout\"] = True\n",
    "figure, axes = plt.subplots(1, 2)\n",
    "sns.histplot(cleaned_house_price, x='price', ax=axes[0])\n",
    "sns.boxplot(cleaned_house_price, y='price', ax=axes[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "房价呈右偏态分布，说明数据集中的大多数房子价格中等，但有一些价格很高的极端值，使得均值被拉高。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 面积分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axes = plt.subplots(1, 2)\n",
    "sns.histplot(cleaned_house_price, x='area', ax=axes[0])\n",
    "sns.boxplot(cleaned_house_price, y='area', ax=axes[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "面积的分布与房价相似，也呈右偏态分布。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 房价与面积的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x350 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(cleaned_house_price, x='area', y='price')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从散点图来看，能大致看出一些正相关关系，但关系的强度需要后续通过计算相关性来得到。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 卧室数与房价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axes = plt.subplots(1, 2)\n",
    "sns.histplot(cleaned_house_price, x='bedrooms', ax=axes[0])\n",
    "sns.barplot(cleaned_house_price, x='bedrooms', y='price', ax=axes[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "此数据集中房子的卧室数范围为1-6个，其中大多房子有2-4个。\n",
    "\n",
    "从平均房价与卧室数之间的柱状图来看，当卧室数小于5个时，卧室数多的房子价格也相应高，但一旦多于5个，房价并不一定相应更高。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 洗手间数与房价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axes = plt.subplots(1, 2)\n",
    "sns.histplot(cleaned_house_price, x='bathrooms', ax=axes[0])\n",
    "sns.barplot(cleaned_house_price, x='bathrooms', y='price', ax=axes[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据集中房子洗手间数量最少1个，最多4个，其中为1个的数量最多。\n",
    "\n",
    "从平均房价与洗手间数之间的柱状图来看，洗手间多的房子价格也相应高。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 楼层数与房价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axes = plt.subplots(1, 2)\n",
    "sns.histplot(cleaned_house_price, x='stories', ax=axes[0])\n",
    "sns.barplot(cleaned_house_price, x='stories', y='price', ax=axes[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "此数据集中房子的楼层数范围为1-4层，其中大多房子有1层或2层。\n",
    "\n",
    "从平均房价与楼层数之间的柱状图来看，楼层多的房子价格也相应高。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 车库数与房价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure, axes = plt.subplots(1, 2)\n",
    "sns.histplot(cleaned_house_price, x='parking', ax=axes[0])\n",
    "sns.barplot(cleaned_house_price, x='parking', y='price', ax=axes[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "此数据集中房子的车库数范围为0-3个，不带车库的房子数量是最多的，其次是1个和2个。\n",
    "\n",
    "从平均房价与楼层数之间的柱状图来看，车库多的房子价格也相应高，但超过2个后，房价并不一定相应更高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
